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        <h3>Contents</h3>
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<li><a class="reference internal" href="#">5.2. Grid Search</a><ul>
<li><a class="reference internal" href="#examples">5.2.1. Examples</a></li>
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  <div class="section" id="grid-search">
<h1>5.2. Grid Search<a class="headerlink" href="#grid-search" title="Permalink to this headline">¶</a></h1>
<p><cite>scikits.learn.grid_search</cite> is a package to optimize
the parameters of a model (e.g. Support Vector Classifier)
using cross-validation.</p>
<p>The computation can be run in parallel using the multiprocessing package.</p>
<p>Main class is <tt class="xref py py-class docutils literal"><span class="pre">GridSearchCV</span></tt>.</p>
<div class="section" id="examples">
<h2>5.2.1. Examples<a class="headerlink" href="#examples" title="Permalink to this headline">¶</a></h2>
<p>See <a class="reference internal" href="../auto_examples/grid_search_digits.html#example-grid-search-digits-py"><em>Parameter estimation using grid search with a nested cross-validation</em></a> for an example of
Grid Search computation on the digits dataset.</p>
<p>See <a class="reference internal" href="../auto_examples/grid_search_text_feature_extraction.html#example-grid-search-text-feature-extraction-py"><em>Sample pipeline for text feature extraction and evaluation</em></a> for an example
of Grid Search coupling parameters from a text documents feature extractor
(n-gram count vectorizer and TF-IDF transformer) with a classifier
(here a linear SVM trained with SGD with either elastic net or L2 penalty).</p>
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